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Electromyography is the study of muscle function through the electrical signals from the muscles. In surface electromyography the electrical signal is detected on the skin. The signal arises from ion exchanges across the muscle fibres’ membranes. The ion exchange in a motor unit, which is the smallest unit of excitation, produces a waveform that is called an action potential (AP). When a sustained contraction is performed the motor units involved in the contraction will repeatedly produce APs, which result in AP trains. A surface electromyogram (EMG) signal consists of the superposition of many AP trains generated by a large number of active motor units. The aim of this dissertation was to introduce and evaluate new methods for analysis of surface EMG signals.

An important aspect is to consider where to place the electrodes during the recording so that the electrodes are not located over the zone where the neuromuscular junctions are located. A method that could estimate the location of this zone was presented in one study.

The mean frequency of the EMG signal is often used to estimate muscle fatigue. For signals with low signal-to-noise ratio it is important to limit the integration intervals in the mean frequency calculations. Therefore, a method that improved the maximum frequency estimation was introduced and evaluated in comparison with existing methods.

The main methodological work in this dissertation was concentrated on finding single motor unit AP trains from EMG signals recorded with several channels. In two studies single motor unit AP trains were enhanced by using filters that maximised the kurtosis of the output. The first of these studies used a spatial filter, and in the second study the technique was expanded to include filtration in time. The introduction of time filtration resulted in improved performance, and when the method was evaluated in comparison with other methods that use spatial and/or temporal filtration, it gave the best performance among them. In the last study of this dissertation this technique was used to compare AP firing rates and conduction velocities in fibromyalgia patients as compared with a control group of healthy subjects.

In conclusion, this dissertation has resulted in new methods that improve the analysis of EMG signals, and as a consequence the methods can simplify physiological research projects.